Drownings in Poland in the years 2013-2021 – trends, changes, inequalities, and preliminary conclusions for public health
Bibliographic record
Abstract
Introduction: Despite progress in the field of water safety, Poland has been experiencing much higher mortality due to drowning than in other countries of the EU region.The main aim of the paper is to examine the changes in drowning frequency and their causes.Material and methods: All available sources of data on drowning in Poland were analysed.Crude and age-standardized mortality rates due to drowning were calculated.A jointpoint regression model was employed in the analysis of long-term trends in annual mortality rates.Results: In total, 7350 persons died due to various types of drowning (ICD-10 codes: W65-W74, V90, V92, X37-X39, X71, Y21) in Poland in the years 2013-2021.The most frequent type of registered drowning was a drowning in natural waters -3990 (54.3% of registered cases).The second cause of death due to drowning were falls into the water -914 (12.4% of cases).Age-standardized death rates of males due to drowning in the years 2013-2021 dropped from 4.3 to 2.7 per 100,000.Among females the age-standardized rate decreased from 0.8 to 0.6 per 100,000.The annual percentage change of mortality (APC) in Poland was -4.0%.The downward trend of mortality was only significant among males (APC = -4.4% vs. APC = -2.6%among females).Mortality reduction was especially high among the youngest age groups: 0-14 years old (APC = -7.9%)and 15-29 years old (APC = -6.5%).Conclusions: There is a need to properly address drowning prevention tailored to groups with risk factors in Poland such as males, elderly people, and people with low socio-economic status.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".